SPOC learner's final grade prediction based on a novel sampling batch normalization embedded neural network method

TL;DR

This paper introduces a novel deep learning approach with sampling batch normalization to accurately predict SPOC learners' final grades despite data imbalance issues.

Contribution

It proposes a new SBNEDNN method that combines data distribution indicators and modified batch normalization to improve grade prediction in imbalanced SPOC datasets.

Findings

SBNEDNN outperforms three other deep learning methods.

The method effectively addresses data imbalance in SPOC grade prediction.

Experimental results validate the superiority of the proposed approach.

Abstract

Recent years have witnessed the rapid growth of Small Private Online Courses (SPOC) which is able to highly customized and personalized to adapt variable educational requests, in which machine learning techniques are explored to summarize and predict the learner's performance, mostly focus on the final grade. However, the problem is that the final grade of learners on SPOC is generally seriously imbalance which handicaps the training of prediction model. To solve this problem, a sampling batch normalization embedded deep neural network (SBNEDNN) method is developed in this paper. First, a combined indicator is defined to measure the distribution of the data, then a rule is established to guide the sampling process. Second, the batch normalization (BN) modified layers are embedded into full connected neural network to solve the data imbalanced problem. Experimental results with other three deep learning methods demonstrates the superiority of the proposed method.